Harnham

Data Scientist

City of London, London
£60000 - £70000 per annum

DATA SCIENTIST

LONDON

£60,000-£70,000 +BONUS +BENEFITS

**DATA SCIENTIST - LONDON - SQL, R, PYTHON - £60,000-£70,000**

THE COMPANY

If you want to work in the city's tech taking a customer-centric approach to analytics this is the role for you!

THE ROLE

As a Data Scientist, you will be taking a proactive approach. Some of your main responsibilities will be:

  • Creation and delivery of customer-first data science-driven through action.
  • Providing actionable insight into the wider business.
  • Providing innovative approaches and solutions.

YOUR SKILLS AND EXPERIENCE

A successful Data Scientist will have:

  • Strong SQL across any database environment.
  • Educated to a degree level in a STEM subject.
  • Programming expertise in Python or R.
  • Have experience of digital and big datasets.

THE BENEFITS

  • A salary of £60,000-£70,000.
  • Comprehensive bonus and benefits package.
  • Innovative work environment.

HOW TO APPLY

Please register your interest by sending your CV to Lydia via the apply link on this page.

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Harnham blog & news

With over 10 years experience working solely in the Data & Analytics sector our consultants are able to offer detailed insights into the industry.

Visit our Blogs & News portal or check out our recent posts below.

Top Considerations For Campaign Analysts

The phrase Campaign Analyst means many things to many people. A quick Google search alone turns up a variety of titles and job descriptions, united by one common thread; creating merit through customer value, metrics, and consumer insight.  As a rule, Campaign Analysts have become more and more important in every forward-thinking business over the past few years, particularly in B2B marketing departments. But what are the big trends we should expect to see over the next year or so? Recapping the role    Campaign Analysts are the go-to resource for everyone from staff to stakeholders, acting as advisor in regard to digital campaigns segmentation and analytical needs, and utilising their comprehensive knowledge and understanding of customer Data. They are often the bridge across departmental teams and help provide a big picture scope, while also diving into the details of customer and marketing insights to achieve actionable results. As an overview, they:     Develop guidelines and build Database procedures to evaluate individual, multi-and omnichannel testing methodologies. Mine Data and reach out to customer segments for query Data.Identify areas of improvement for campaign organisation.Provide critical support to campaigns, from conception to implementation, with the ability to translate marketing plans into production-ready endeavours. Is it time for a Data Health Check?  Though Data & Analytics have played a big role in retail over the last few years, Marketing departments have often lagged a bit behind. However, they’re beginning to catch up. In 2018, a Dun & Bradstreet survey showed over 60 percent of B2B companies thought Data quality was extremely important. In 2019, the same rings true.  But, as things develop even further, here are a few things to consider in your Marketing Data ecosystems:  Updating your company’s customer Data health – Eliminate duplicate records, make sure customer information is accurate and up-to-date, and make sure your segmentations still make sense. Have decision makers for targeted campaigns changed or moved on? Are email addresses and phone numbers still valid? Keeping your customer Data up-to-date ensures you’re not analysing invalid Data.  Creating a more focused Data-Driven approach with Marketing ROI – Assess and evaluate spending and revenue with a lens on Marketing channel variety. What’s working? What isn’t? Where can you cut costs while increasing market-generated revenue?Micro-segmenting your targeting efforts – Go deeper in your targeted Marketing efforts. Narrow your Data parameters. The more layers you add, the more targeted you can focus your messaging, and the more likely you are to reach the best audience for your business.Utilising video Marketing – Much like boosting your resume with video, the same can be said for targeted Marketing campaigns. As a Campaign Analyst, you’ll need to work with departments across the business to create a unified Content Marketing Strategy and video remains a crucial format according to a LinkedIn study. And on that note… Pressing play on video The bland days of corporate speak, restrictive tone, and limited colour in video Marketing are a thing of the past. Video is now a crucial Marketing and engagement tool, providing the opportunity to engage with customers in almost real-time.   With this, customers feel heard and can help transform a product as well as gain trust in the business itself. From GDPR to fake news to Data breaches, it’s highly important for businesses to provide transparency in their interactions and video helps them do that.  By being transparent and acknowledging mistakes, business can both gain consumer trust and improve upon their mistakes.. In addition, customer’s no longer feel like voices in a canyon; they are part of the business and therefore more invested in the company’s success.  Additionally, this can expand your reach, allowing you discover what a broader range of customers think. If we want a recommendation for a good restaurant, a doctor, or some other place or service, we turn first to our friends. And in the era of social media, our friends are everywhere. What better way to reach out to your target audience than by video with authentic customer reviews and testimonials?  A well-developed strategy leads to happy customers and their goodwill is, ultimately, free Marketing.  If you’re looking for your next Campaign Analyst opportunity, we may have a role for you. Check out our latest roles or get in touch with one of our specialist consultants. 

Lloyd Dunstall

13. March 2019

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“Disruptive Approaches Are Driving Change” Derek Dempsey On The Fraud Industry: Part Two

This is the second part of our interview with Derek Dempsey, Fraud Analytics Director for FICO. To read the first part click here. How have Data & Analytics impacted the detection, and prevention, of Fraud?  Big Data is interesting. We want to use as much Data as possible but you have to be sure the Data you have is reliable and robust. Bad Data could lead to significant issues for anyone in Fraud detection.  But really, Data & Analytics are the fuel that drives effective Fraud detection. Our world has used these tools very effectively for many years and we have always been at the forefront in the use of new techniques. Effective Fraud detection relies heavily on using the best Data available and the right analytical techniques to extract useful information. Furthermore, it is constantly evolving as more information becomes available.  What impact do you think Machine Learning and AI can have on Fraud prevention and detection?  In the last few years we’ve seen an explosion of new interest and new techniques, with lots of new players coming into the market.  Companies like FICO have been using Machine Learning for Fraud detection for 25 years so this isn't new. What is new is a whole generation of new technologies, new companies and disruptive approaches that have been driving change.  For us, apart from increased competitive pressures, one of the other big changes is that companies have built large Data Science teams and have a bigger appetite to build their own solutions using open source technologies. However, it’s one thing building these great models, it’s another getting them to operate effectively and correctly in the real world. The level of governance that we are subject to is enormous just to make sure that our models perform as they should. This will be a challenge to the newcomers. They are here to stay though, and should drive better performance and better Fraud detection.  Where AI techniques are set to make an impact is in AML and compliance monitoring. These have used rule-based techniques due to regulatory pressures, but it is clear that more advanced techniques are required to provide better detection of money laundering and terrorist financing. However, businesses do require us to provide explainability but regulators are saying the will look favourably on AI usage if it can provide this. The more AI techniques are used, the more this issue of explainability is going to be important.  Why is the development of analytics, tools and techniques so important within different industries? I think domain knowledge of the Data and business will always be important. 'Specialisms' occur and always will. The analytic techniques, the tools and languages can be standard although some are more appropriate for different specialities. However, the differences in Data and business model always results in specialised applications to address this.  For example, something like Card Fraud or AML require techniques that can analyse and process huge volumes of transactions, whereas something like Claims fraud or Application Fraud won't have this requirement and other factors can be more relevant. However, there is little doubt that Financial Services and Telco have actively sought AI technologies while others have been more fearful of so-called 'black box' approaches.  How are Big Data and Data Science tools, such as Hadoop, helping combat Fraud effectively?  There’s no question from an analytics perspective that Python and R are the two languages that Data Scientists are using. But it helps to have specialists in technical skills, such as DevOps and DataOps, to provide the technical expertise that allows them to build models and utilise Data most effectively.  As for Hadoop, it makes sense for Data Scientists to have an understanding, but ultimately I see that skillset as one belonging to the technical specialists. This is why I firmly believe that every Data Science team should have a supporting tech function.   What are the latest technologies helping combat Fraud?  From an analytical perspective there has been a recent focus on graph analytics and network analytics as these can be applied to external Data. These approaches have been around for a while however and are limited to certain types of Fraud. We’re also seeing more unsupervised techniques being used as these do not rely on prior fraud case data so can be applied to a wider set of cases.  Another new area has been adaptivity. This is a model that adapts over time depending on operational feedback from the analysts. Again this is not new but you really need to balance the impact of this new information compared to how the model is currently working and so is very challenging. As long as you can maintain a sufficient degree of explainability you can ensure the process is sufficiently well governed.  There has been a significant move to cloud-based technologies where companies can reduce their implementation and maintenance costs. We are just about to release our new Falcon X platform, which is a cloud-based fraud platform, that will allow clients to use all the FICO capabilities but also allow them to develop their own analytics as well.  I think potentially the biggest change will be the progressive adoption of biometric authentication. SCA is a requirement for all high-value transactions in PSD2 and this requires two-factor authentication from inherence, ownership and knowledge: so something you are, something you have or something you know. I think biometrics will start to play a big role in authentication and, hopefully, will provide much greater identity security.  Another trend I think we may see is the growth of digital IDs. There is already a well-advanced program in the Nordics called BankID and the concept of a digital ID seems inevitable at some point.  Derek spoke to Senior Consultant, Rosalind Madge. Get in touch with Rosalind or take a look at our latest job opportunities here.

Rosalind Madge

28. February 2019

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